{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(397, 9)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  year  \\\n",
       "0  18.0          8         307.0        130    3504          12.0    70   \n",
       "1  15.0          8         350.0        165    3693          11.5    70   \n",
       "2  18.0          8         318.0        150    3436          11.0    70   \n",
       "3  16.0          8         304.0        150    3433          12.0    70   \n",
       "4  17.0          8         302.0        140    3449          10.5    70   \n",
       "\n",
       "   origin                       name  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'E:\\programming\\dataset\\Into_to_statstical_learning\\Auto.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             float64\n",
       "cylinders         int64\n",
       "displacement    float64\n",
       "horsepower       object\n",
       "weight            int64\n",
       "acceleration    float64\n",
       "year              int64\n",
       "origin            int64\n",
       "name             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['130', '165', '150', '140', '198', '220', '215', '225', '190',\n",
       "       '170', '160', '95', '97', '85', '88', '46', '87', '90', '113',\n",
       "       '200', '210', '193', '?', '100', '105', '175', '153', '180', '110',\n",
       "       '72', '86', '70', '76', '65', '69', '60', '80', '54', '208', '155',\n",
       "       '112', '92', '145', '137', '158', '167', '94', '107', '230', '49',\n",
       "       '75', '91', '122', '67', '83', '78', '52', '61', '93', '148',\n",
       "       '129', '96', '71', '98', '115', '53', '81', '79', '120', '152',\n",
       "       '102', '108', '68', '58', '149', '89', '63', '48', '66', '139',\n",
       "       '103', '125', '133', '138', '135', '142', '77', '62', '132', '84',\n",
       "       '64', '74', '116', '82'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['horsepower'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['horsepower'] = data['horsepower'].replace('?',np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['horsepower'] = data['horsepower'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             float64\n",
       "cylinders         int64\n",
       "displacement    float64\n",
       "horsepower        int32\n",
       "weight            int64\n",
       "acceleration    float64\n",
       "year              int64\n",
       "origin            int64\n",
       "name             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "powers = np.arange(1,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors = []\n",
    "for power in powers:\n",
    "    poly = PolynomialFeatures(power)\n",
    "    X = poly.fit_transform(data['horsepower'].to_frame())\n",
    "    y = data['mpg']\n",
    "    X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.1,random_state = 0)\n",
    "\n",
    "    lr = LinearRegression()\n",
    "    lr.fit(X_train,y_train)\n",
    "    errors.append(mean_squared_error(y_test,lr.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'LOOCV')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = pd.DataFrame({\"power\":powers,'Errors':errors})\n",
    "plt.figure(figsize = (12,6))\n",
    "g = sns.FacetGrid(data = temp,size=5)\n",
    "g.map(plt.scatter, 'power' , 'Errors')\n",
    "g.map(plt.plot, 'power', 'Errors')\n",
    "plt.title('LOOCV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors_list = []\n",
    "from sklearn.utils import shuffle\n",
    "for i in range(10):\n",
    "    errors = []\n",
    "    n = len(data)\n",
    "    for power in powers:\n",
    "        poly = PolynomialFeatures(power)\n",
    "        X = poly.fit_transform(data['horsepower'].to_frame())\n",
    "        y = data['mpg']\n",
    "        X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.1,random_state = i)\n",
    "\n",
    "        lr = LinearRegression()\n",
    "        lr.fit(X_train,y_train)\n",
    "        errors.append(mean_squared_error(y_test,lr.predict(X_test)))\n",
    "    errors_list.append(errors)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "powers = np.arange(1,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = {'Errors'+ str(i):errors_list[i] for i in range(10)}\n",
    "temp = pd.DataFrame(data_dict)\n",
    "temp.index = powers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Errors0</th>\n",
       "      <th>Errors1</th>\n",
       "      <th>Errors2</th>\n",
       "      <th>Errors3</th>\n",
       "      <th>Errors4</th>\n",
       "      <th>Errors5</th>\n",
       "      <th>Errors6</th>\n",
       "      <th>Errors7</th>\n",
       "      <th>Errors8</th>\n",
       "      <th>Errors9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.811143</td>\n",
       "      <td>25.650160</td>\n",
       "      <td>28.827536</td>\n",
       "      <td>24.301369</td>\n",
       "      <td>19.272390</td>\n",
       "      <td>27.642677</td>\n",
       "      <td>23.776677</td>\n",
       "      <td>19.308620</td>\n",
       "      <td>21.060574</td>\n",
       "      <td>19.866369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.977120</td>\n",
       "      <td>17.264520</td>\n",
       "      <td>23.742558</td>\n",
       "      <td>16.833850</td>\n",
       "      <td>13.476055</td>\n",
       "      <td>24.596552</td>\n",
       "      <td>17.410193</td>\n",
       "      <td>16.455937</td>\n",
       "      <td>12.453263</td>\n",
       "      <td>15.961779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.915739</td>\n",
       "      <td>17.108497</td>\n",
       "      <td>23.604935</td>\n",
       "      <td>16.904634</td>\n",
       "      <td>13.398018</td>\n",
       "      <td>24.738484</td>\n",
       "      <td>17.722400</td>\n",
       "      <td>17.946887</td>\n",
       "      <td>12.251760</td>\n",
       "      <td>15.760263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.778294</td>\n",
       "      <td>16.927266</td>\n",
       "      <td>24.277251</td>\n",
       "      <td>17.061203</td>\n",
       "      <td>13.286756</td>\n",
       "      <td>24.676498</td>\n",
       "      <td>18.807723</td>\n",
       "      <td>17.703585</td>\n",
       "      <td>12.478693</td>\n",
       "      <td>16.198389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.938348</td>\n",
       "      <td>16.824453</td>\n",
       "      <td>25.036717</td>\n",
       "      <td>16.354698</td>\n",
       "      <td>12.552333</td>\n",
       "      <td>24.498935</td>\n",
       "      <td>16.656960</td>\n",
       "      <td>15.593667</td>\n",
       "      <td>12.468997</td>\n",
       "      <td>15.313392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Errors0    Errors1    Errors2    Errors3    Errors4    Errors5  \\\n",
       "1  21.811143  25.650160  28.827536  24.301369  19.272390  27.642677   \n",
       "2  15.977120  17.264520  23.742558  16.833850  13.476055  24.596552   \n",
       "3  15.915739  17.108497  23.604935  16.904634  13.398018  24.738484   \n",
       "4  15.778294  16.927266  24.277251  17.061203  13.286756  24.676498   \n",
       "5  15.938348  16.824453  25.036717  16.354698  12.552333  24.498935   \n",
       "\n",
       "     Errors6    Errors7    Errors8    Errors9  \n",
       "1  23.776677  19.308620  21.060574  19.866369  \n",
       "2  17.410193  16.455937  12.453263  15.961779  \n",
       "3  17.722400  17.946887  12.251760  15.760263  \n",
       "4  18.807723  17.703585  12.478693  16.198389  \n",
       "5  16.656960  15.593667  12.468997  15.313392  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2591c5eed30>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.plot.line(figsize = (12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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